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a616e56 cdc317a a616e56 cdc317a c722f9c cdc317a a616e56 cdc317a a616e56 cdc317a a616e56 cdc317a a616e56 cdc317a a616e56 cdc317a a616e56 c722f9c a616e56 c722f9c cdc317a a616e56 cdc317a a616e56 cdc317a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | import random
import numpy as np
import torch
from PIL import Image
from config import CLASSICAL_MODEL_TYPES
from data_utils import get_eval_transform, prepare_splits, get_class_names
from train_utils import load_model, get_runtime_device, _load_meta
def _extract_feature(image: Image.Image, device: torch.device) -> np.ndarray:
from backbone_utils import load_backbone
backbone = load_backbone(device)
backbone.eval()
tensor = get_eval_transform()(image.convert("RGB")).unsqueeze(0).to(device)
with torch.no_grad():
feat = backbone(tensor)
return feat.cpu().numpy()
def predict_uploaded_image(model_name: str, image: Image.Image):
if not model_name:
return "Veuillez sélectionner un modèle.", None
if image is None:
return "Veuillez importer une image.", None
meta = _load_meta(model_name)
model_type = meta["config"].get("model_type", "cnn")
class_names = meta["config"]["class_names"]
device = get_runtime_device()
if model_type in CLASSICAL_MODEL_TYPES:
from classical_ml_utils import load_classical_pipeline
pipeline = load_classical_pipeline(model_name)
feat = _extract_feature(image, device)
probs = pipeline.predict_proba(feat)[0].tolist()
pred_idx = int(np.argmax(probs))
else:
model, _ = load_model(model_name, device)
tensor = get_eval_transform()(image.convert("RGB")).unsqueeze(0).to(device)
with torch.no_grad():
logits = model(tensor)
probs = torch.softmax(logits, dim=1).squeeze(0).cpu().tolist()
pred_idx = int(torch.argmax(logits, dim=1).item())
result_text = (
f"Prédiction : {class_names[pred_idx]}\n"
f"Confiance : {max(probs):.4f}\n\n"
f"Modèle : {model_name}\n"
f"Type : {model_type}\n"
f"Appareil : {device}"
)
prob_dict = {class_names[i]: float(probs[i]) for i in range(len(class_names))}
return result_text, prob_dict
def test_random_sample(model_name: str):
if not model_name:
return None, "Veuillez sélectionner un modèle.", None
meta = _load_meta(model_name)
model_type = meta["config"].get("model_type", "cnn")
class_names = get_class_names()
device = get_runtime_device()
splits = prepare_splits()
test_dataset = splits["test"]
idx = random.randint(0, len(test_dataset) - 1)
item = test_dataset[idx]
image = item["image"]
if not isinstance(image, Image.Image):
image = Image.open(image)
image = image.convert("RGB")
label_name = class_names[int(item["label"])]
if model_type in CLASSICAL_MODEL_TYPES:
from classical_ml_utils import load_classical_pipeline
pipeline = load_classical_pipeline(model_name)
feat = _extract_feature(image, device)
probs = pipeline.predict_proba(feat)[0].tolist()
pred_idx = int(np.argmax(probs))
else:
model, _ = load_model(model_name, device)
tensor = get_eval_transform()(image).unsqueeze(0).to(device)
with torch.no_grad():
logits = model(tensor)
probs = torch.softmax(logits, dim=1).squeeze(0).cpu().tolist()
pred_idx = int(torch.argmax(logits, dim=1).item())
model_class_names = meta["config"]["class_names"]
result_text = (
f"Échantillon test aléatoire\n"
f"Vérité terrain : {label_name}\n"
f"Prédiction : {model_class_names[pred_idx]}\n"
f"Confiance : {max(probs):.4f}\n"
f"Type modèle : {model_type}\n"
f"Appareil : {device}"
)
prob_dict = {model_class_names[i]: float(probs[i]) for i in range(len(model_class_names))}
return image, result_text, prob_dict
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